相关实验视频
Updated: Sep 13, 2025

13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
20.1K
在横截面数据中的节点可预测性在预测性重犯中不超过机械总和
Daphne Jonkers Both1,2, Kelly M Babchishin3, Yvonne H A Bouman1
1Stichting Transfore, forensic outpatient clinic De Tender, Deventer, The Netherlands.
Assessment
|July 29, 2025
概括
预测性犯罪的常规总分方法比考虑风险因素相互关系的模型更准确. 这一发现支持了评估成年男性再犯风险的传统方法.
科学领域:
- 法医心理学 法医心理学
- 犯罪学 犯罪学
- 风险评估 风险评估
背景情况:
- 预测性重犯对于公共安全和罪犯管理至关重要.
- 动态风险因素通常用于风险评估工具,如STABLE-2007.
- 了解风险因素之间的相互关系可能会提高预测准确性.
研究的目的:
- 为了比较两种评估性再犯风险的方法的预测准确性:机械总和 (总分) 和节点可预测性.
- 评估在预测再犯案时,计算动态风险因素之间的相互关系的有效性.
主要方法:
- 利用了5315名北美男性的数据集,这些男性的风险因子被评估为STABLE-2007的动态风险因子.
- 采用300次交叉验证方法,将数据分成20:80培训和测试集.
- 计算曲线下的面积 (AUC) 来测量这两种方法的预测准确性.
主要成果:
- 与节点预测性 (AUC=0.50,SD=0.03) 相比,机械总和显示出明显更高的预测准确性 (AUC=0.67,SD=0.04).
- 在AUC的差异是统计学上显著的 (t(299) =80.2,p <.001),具有很大的效果大小 (科恩的d=4.63).
结论:
- 总结动态风险因素 (机械总和) 的传统方法优于节点可预测性,用于评估群体级别的性再犯风险.
- 未来的研究应该研究将时间效应,个体差异和网络中心性纳入节点模型,以潜在地提高它们的准确性.
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